Transcription activator-like (TAL) effector domains were recently discovered to be DNA binding domains. Based on the reported DNA recognition features of the these domains, long arrays of repeats that each recognize one base pair using only two variable amino acids, the structure of this DNA binding domain is unlike any that have been previously described. As such, the protein fold and structural features responsible for specific DNA recognition are highly novel and worthy of elucidation for their own merits. However, the seeming simple recognition code and apparent recognition of a wide spectrum of sequences has important implications as a scaffold for engineering new DNA binding proteins. TAL domains appear to be even more flexible in their recognition properties than zinc fingers (ZFs), the engineering of which have had transformative impact in the areas of gene regulation, genome engineering, genetically modified organisms, and gene therapy. Despite their successes, the difficultly of engineering high-quality ZFs represents a significant bottleneck to their widespread application. We hypothesize that TAL domains will have superior performance characteristics compared to the gold-standard ZF domains for engineering novel DNA- binding proteins. We will test our hypothesis using a combined computational and biochemical approach to examine the protein fold and repeat assembly (Aim 1), elucidate the mechanism and extent of DNA recognition (Aim 2), and investigate the potential of TAL domains for the creation of sequence-specific tools for gene regulation and genome engineering (Aim 3). If successful, this study will provide insights into the structure and function of a novel DNA-binding domain, and an understanding of how those insights can be applied to create tools for genetic modification that would be more broadly accessible and of greater general utility than current methods. KEY WORDS Protein-nucleic acid interactions, engineered zinc fingers, ab initio modeling, protein structure, protein folding, structure-function relationship, genome engineering, gene therapy, computational design.